Jamshid Abouei — accepted AI-conference papers

2026

The Missing Point in Vision Transformers for Universal Image Segmentation

Sajjad Shahabodini, Mobina Mansoori, Farnoush Bayatmakou, Jamshid Abouei, Konstantinos Plataniotis, Arash Mohammadi

CVPR 2026

Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based approaches yield high-quality masks by capturing global context. However, accurately classifying these masks, especially in the presence of ambiguous bound

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2025

Self-Prompting Polyp Segmentation in Colonoscopy Using Hybrid YOLO-SAM2 Model

Mobina Mansoori, Sajjad Shahabodini, Jamshid Abouei, Konstantinos N. Plataniotis, Arash Mohammadi

ICASSP 2025accepted

Early diagnosis and treatment of polyps during colonoscopy are essential for reducing the incidence and mortality of Colorectal Cancer (CRC). However, the variability in polyp characteristics and the presence of artifacts in colonoscopy images and videos pose significant challenges for accurate and…

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2023

ViT-Cat: Parallel Vision Transformers With Cross Attention Fusion for Popularity Prediction in MEC Networks

Zohreh Hajiakhondi-Meybodi, Arash Mohammadi, Ming Hou, Jamshid Abouei, Konstantinos N. Plataniotis

ICASSP 2023accepted

Mobile Edge Caching (MEC) is a revolutionary technology for the Sixth Generation (6G) of wireless networks with the promise to significantly reduce users’ latency via offering storage capacities at the edge of the network. The efficiency of the MEC network, however, critically depends on its ability…

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2015

Accurate kernel-based spectrum sensing for Gaussian and non-Gaussian noise models

Argin Margoosian, Jamshid Abouei, Konstantinos N. Plataniotis

ICASSP 2015accepted

This paper introduces a spectrum sensing scenario based on kernel theory which compares favorably against the conventional Energy Detector (ED) in a cognitive radio system. The so-called Kerenlized Energy Detector (KED) can provide superior accuracy in the case of non-Gaussian noise. The incorporati…

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